TL;DR
This paper introduces the Dynamic Bowser Routing Problem, a new model for optimizing refuelling operations on construction sites using data-driven decision support models to minimize travel distance and prevent fuel shortages.
Contribution
It formulates and solves deterministic and stochastic variants of the Dynamic Bowser Routing Problem with scalable mathematical programming models, validated through extensive computational experiments.
Findings
Models effectively minimize bowser travel distance.
Approaches prevent assets from running out of fuel.
Validated with real site data and project partner inputs.
Abstract
We investigate opportunities offered by telematics and analytics to enable better informed, and more integrated, collaborative management decisions on construction sites. We focus on efficient refuelling of assets across construction sites. More specifically, we develop decision support models that, by leveraging data supplied by different assets, schedule refuelling operations by minimising the distance travelled by the bowser truck as well as fuel shortages. Motivated by a practical case study elicited in the context of a project we recently conducted at Crossrail, we introduce the Dynamic Bowser Routing Problem. In this problem the decision maker aims to dynamically refuel, by dispatching a bowser truck, a set of assets which consume fuel and whose location changes over time; the goal is to ensure that assets do not run out of fuel and that the bowser covers the minimum possible…
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